Software Alchemy: Turning Complex Statistical Computations into Embarrassingly-Parallel Ones

TL;DR

This paper introduces a method to transform complex statistical algorithms into embarrassingly parallel forms, significantly improving computational speed and overcoming memory limitations across various hardware platforms.

Contribution

The paper presents a general technique for converting non-embarrassingly parallel statistical algorithms into EP algorithms, enhancing scalability and efficiency.

Findings

Achieved substantial speedups in diverse statistical computations.

Overcame memory constraints in parallel processing environments.

Demonstrated broad applicability across multiple platforms.

Abstract

The growth in the use of computationally intensive statistical procedures, especially with Big Data, has necessitated the usage of parallel computation on diverse platforms such as multicore, GPU, clusters and clouds. However, slowdown due to interprocess communication costs typically limits such methods to "embarrassingly parallel" (EP) algorithms, especially on non-shared memory platforms. This paper develops a broadly-applicable method for converting many non-EP algorithms into statistically equivalent EP ones. The method is shown to yield excellent levels of speedup for a variety of statistical computations. It also overcomes certain problems of memory limitations.